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Artificial Intelligence (AI)-Based Diagnosis in Clinical Practice
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Artificial Intelligence (AI)-Based Diagnosis in Clinical Practice

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Epidemiology & Public Health".

Deadline for manuscript submissions: 24 July 2025 | Viewed by 3591

Special Issue Editor

Dr. Takao Fujisawa

E-Mail Website
Guest Editor
Division of Allergy, National Hospital Organization Mie National Hospital, Tsu, Japan
Interests: clinical immunology; allergy; pediatrics; eosinophils; food allergy; asthma; atopic dermatitis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

AI is now changing the world. In the field of medicine, new AI diagnostic systems are also changing the conventional wisdom of medical practice. In particular, considerable progress has been made in the field of diagnostic imaging. However, the system from electronic medical record (EMR) data to AI diagnosis is still in its infancy. In this Special Issue, we call for contributions presenting the latest research on AI diagnostics in fields other than imaging: how to clean up unstructured EMR data and incorporate them into AI models such as deep learning, the networking of EMRs, coupling EMRs with big data from insurance databases, and other related topics.

Dr. Takao Fujisawa
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • AI
  • diagnosis
  • clinical medicine
  • practice

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Published Papers (3 papers)

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Research

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24 pages, 2788 KiB  
Article
AI-Driven Prediction of Glasgow Coma Scale Outcomes in Anterior Communicating Artery Aneurysms
by Corneliu Toader, Octavian Munteanu, Mugurel Petrinel Radoi, Carla Crivoi, Razvan-Adrian Covache-Busuioc, Matei Serban, Alexandru Vlad Ciurea and Nicolaie Dobrin
J. Clin. Med. 2025, 14(8), 2672; https://doi.org/10.3390/jcm14082672 - 14 Apr 2025
Viewed by 510
Abstract
Background: The Glasgow Coma Scale (GCS) is a cornerstone in neurological assessment, providing critical insights into consciousness levels in patients with traumatic brain injuries and other neurological conditions. Despite its clinical importance, traditional methods for predicting GCS scores often fail to capture [...] Read more.
Background: The Glasgow Coma Scale (GCS) is a cornerstone in neurological assessment, providing critical insights into consciousness levels in patients with traumatic brain injuries and other neurological conditions. Despite its clinical importance, traditional methods for predicting GCS scores often fail to capture the complex, multi-dimensional nature of patient data. This study aims to address this gap by leveraging machine learning (ML) techniques to develop accurate, interpretable models for GCS prediction, enhancing decision making in critical care. Methods: A comprehensive dataset of 759 patients, encompassing 25 features spanning pre-, intra-, and post-operative stages, was used to develop predictive models. The dataset included key variables such as cognitive impairments, Hunt and Hess scores, and aneurysm dimensions. Six ML algorithms, including random forest (RF), XGBoost, and artificial neural networks (ANN), were trained and rigorously evaluated. Data preprocessing involved numerical encoding, standardization, and stratified splitting into training and validation subsets. Model performance was assessed using accuracy and receiver operating characteristic area under the curve (ROC AUC) metrics. Results: The RF model achieved the highest accuracy (86.4%) and mean ROC AUC (0.9592 ± 0.0386, standard deviation), highlighting its robustness and reliability in handling heterogeneous clinical datasets. XGBoost and SVM models also demonstrated strong performance (ROC AUC = 0.9502 and 0.9462, respectively). Key predictors identified included the Hunt and Hess score, aneurysm dimensions, and post-operative factors such as prolonged intubation. Ensemble methods outperformed simpler models, such as K-nearest neighbors (KNN), which struggled with high-dimensional data. Conclusions: This study demonstrates the transformative potential of ML in GCS prediction, offering accurate and interpretable tools that go beyond traditional methods. By integrating advanced algorithms with clinically relevant features, this work provides a dynamic, data-driven framework for critical care decision making. The findings lay the groundwork for future advancements, including multi-modal data integration and broader validation, positioning ML as a vital tool in personalized neurological care. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI)-Based Diagnosis in Clinical Practice)
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11 pages, 1104 KiB  
Article
Identification of Predictors of Sarcopenia in Older Adults Using Machine Learning: English Longitudinal Study of Ageing
by Nieves Pavón-Pulido, Ligia Dominguez, Jesús Damián Blasco-García, Nicola Veronese, Ana-María Lucas-Ochoa, Emiliano Fernández-Villalba, Ana-María González-Cuello, Mario Barbagallo and Maria-Trinidad Herrero
J. Clin. Med. 2024, 13(22), 6794; https://doi.org/10.3390/jcm13226794 - 12 Nov 2024
Cited by 1 | Viewed by 1520
Abstract
Background: After its introduction in the ICD-10-CM in 2016, sarcopenia is a condition widely considered to be a medical disease with important consequences for the elderly. Considering its high prevalence in older adults and its detrimental effects on health, it is essential to [...] Read more.
Background: After its introduction in the ICD-10-CM in 2016, sarcopenia is a condition widely considered to be a medical disease with important consequences for the elderly. Considering its high prevalence in older adults and its detrimental effects on health, it is essential to identify its risk factors to inform targeted interventions. Methods: Taking data from wave 2 of the ELSA, using ML-based methods, this study investigates which factors are significantly associated with sarcopenia. The Minimum Redundancy Maximum Relevance algorithm has been used to allow for an optimal set of features that could predict the dependent variable. Such a feature is the input of a ML-based prediction model, trained and validated to predict the risk of developing or not developing a disease. Results: The presented methods are suitable to identify the risk of acquired sarcopenia. Age and other relevant features related with dementia and musculoskeletal conditions agree with previous knowledge about sarcopenia. The present classifier has an excellent performance since the “true positive rate” is 0.81 and the low “false positive rate” is 0.26. Conclusions: There is a high prevalence of sarcopenia in elderly people, with age and the presence of dementia and musculoskeletal conditions being strong predictors. The new proposed approach paves the path to test the prediction of the incidence of sarcopenia in older adults. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI)-Based Diagnosis in Clinical Practice)
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Review

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8 pages, 187 KiB  
Review
Use of Artificial Intelligence in Difficult Airway Assessment: The Current State of Knowledge
by Mateusz Wilk, Wojciech Pikiewicz, Krzysztof Florczak and Dawid Jakóbczak
J. Clin. Med. 2025, 14(5), 1602; https://doi.org/10.3390/jcm14051602 - 27 Feb 2025
Cited by 1 | Viewed by 956
Abstract
Artificial Intelligence (AI) has become one of the most transformative technologies of the 21st century. It is poised to reshape medicine, as almost every field of hospital treatment has seen an increase in AI’s presence. In this article, we focus on its impact [...] Read more.
Artificial Intelligence (AI) has become one of the most transformative technologies of the 21st century. It is poised to reshape medicine, as almost every field of hospital treatment has seen an increase in AI’s presence. In this article, we focus on its impact in the field of anesthesia. We discuss its possible influence on difficult airway management, as it remains one of the most critical and potentially hazardous aspects of anesthesia, often leading to life-threatening complications. The accurate prediction of difficult airways can significantly improve patient safety. We covered the available literature on AI-based models for difficult airway prediction in comparison to traditional forms of airway assessment, as well as the predictive value of ultrasonography. We also address the narrative that AI-based algorithms show high sensitivity and specificity, with which they significantly outperform classical tests based on complex scales and indices. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI)-Based Diagnosis in Clinical Practice)
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